Strategies for Successful Data Mining

Data Mining Services are a boon for many executives in the private and public sectors because it is the key to good customer relationship management. It also helps in effective manufacturing and minimizes the risks in business. Even though compared to data warehousing and ERP (enterprise resource planning), data mining is more recent, it is more effective in predicting the future.

Earlier investments in ERP solutions were based on the past data and its analysis which explains why certain things happened. But data mining is not just OLAP (online analytical processing). It can explain not only how but even why certain things happened and helps in taking better decisions to shape the future.

Data mining is actually mining through lots of data to come up with some useful information. Useful information can be defined only after clear objectives are defined. And of course! The data has to be good. There are three basic sources for data:

Purchased data

Collected data

Transaction data (additional details of customers)

Successful data mining should have three types of analytical capabilities:

Reporting past information

Classifying customer types according to behavioral groups

Forecasting sales region/ time wise

Deployment of the mined information to decision makers, databases and operation systems is critical to successful data mining. Data mining projects can be outsourced very successfully. It is imperative that those decision makers along with the IT staff who use the mined information be trained to update and manage the system.

About Rajeev R

Manages the day-to-day operations of MOS from NY. With an interest in information technology, Rajeev has guided MOS to extensive use of digital technology and the internet that benefits MOS as well as MOS clients.
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